MOF-derived Nanocomposites-based Mass Spectrometry Metabolic Analysis For Cancer Diagnosis

Precision cancer diagnosis is crucial for improving patients’five-year survival rate and quality of life.Metabolomics,which focuses on studying metabolites at the end products in bio-processes,provides phenotype-relevant information for cancer diagnosis.Laser desorption/ionization time-of-flight mass spectrometry（LDI MS）has unique advantages,such as low cost,high throughput,and simple sample preparation,making it a promising technique for clinical metabolomics analysis.Of note,the rational design of a high-performance matrix is a prerequisite for clinical metabolic profiling using LDI MS.The inorganic matrices have been customized as matrices for LDI-MS detection of metabolites.They still face lower selectivity and sensitivity for clinical samples.The regulation of the physical effects,such as surface roughness,hollow structure,and heterostructures,can effectively optimize the properties of the size exclusion,light response,charge transfer,and thermal conductivity of the matrix and thereby enhance the selectivity and sensitivity for LDI MS in clinical metabolomics analysis.Moreover,studying the physical effects of matrices can help us understand the mechanism of LDI MS.Metal-organic frameworks（MOFs）,with easy synthesis,good stability,tunability,and diverse topologies,are a favorable choice for constructing matrices with multiple physical effects.Therefore,in our work,we synthesized a series of novel MOF-based matrices with various physical effects and investigated the selectivity and sensitivity of those matrices for LDI MS metabolic analysis.The high-performance MOF-based composite matrix was successfully applied for metabolite-assisted diagnosis of gynecological and liver cancers.Firstly,we synthesized binary physical effects Fe OOH@ZIF-8（rough surface,morphology,and heterojunction structure）with core-satellite structure by a simple liquid-phase reaction.The ZIF-8 satellites can selectively enrich small-molecule metabolites from serum through size-exclusion effects.Additionally,Fe OOH@ZIF-8with heterojunction components exhibits significantly enhanced charge transfer capability,enabling sensitive detection of metabolites（～8.5 pmol）.In cancer diagnosis,the Fe OOH@ZIF-8-assisted LDI-MS achieved rapid metabolic fingerprints of the serum of the gynecological cancers（ovarian,endometrial,and cervical cancer）patients without enrichment.Further analysis of extracted serum metabolic fingerprints successfully discriminated patients with gynecological cancers（GCs）from healthy controls and differentiated three major subtypes of GCs（accuracy of 0.9）by combining unsupervised multivariate statistical algorithms.In conclusion,this work has constructed a novel LDI MS platform based on new nanomaterials,which have high sensitivity and throughput and can be used for cancer metabolism-assisted diagnosis.To further enhance the LDI performance of matrix materials,we designed a quadruple physical effects material Mn₂O₃/（Co,Mn）（Co,Mn）₂O₄（MO/CMO）（surface roughness,heterojunction structure,tip,and hollow morphology）.The tip and hollow morphology of the material can significantly enhance the light-trap ability of MO/CMO（≈10-fold compared to the nanoparticles）,further promoting the charge and heat transfer of heterojunction MO/CMO,thus improving the ionization and desorption ability of the matrix.In addition,the surface roughness of MO/CMO can selectively enrich small molecule metabolites in serum,thereby enhancing the selectivity of LDI metabolite analysis.Compared with traditional commercial matrix materials,the MO/CMO matrix with quadruple physical effects can enhance the signal intensity by48-fold.In clinical applications,we used MO/CMO as the matrix for serum metabolite analysis of ovarian tumors.The metabolite analysis combined with machine learning accurately diagnosed early-stage ovarian tumors（85.5%accuracy）.Finally,seven metabolites associated with the progression of ovarian tumors are screened as potential biomarkers,which may provide an accurate diagnosis of early-stage ovarian cancer.Finally,to explore the effect of structural parameters on LDI performance,we studied the influence of the shell number in the hollow material on LDI performance based on surface roughness,heterojunction,and sharp tips.We synthesized single-layer,double-layer,and triple-layer hollow Zn Mn₂O₄/（Co,Mn）（Co,Mn）₂O₄（ZMO/CMO）composites to investigate the photo-response ability through the calcination of MOF-on-MOF.We successfully demonstrated the triple-layer hollow composite has strong light absorption ability through experimental characterization and theoretical calculations.The triple-layer hollow composite showed strong LDI MS performance in detecting standard small molecule metabolites（≈102-fold compared to the commercialized products）.For cancer precision diagnosis,we directly extracted serum metabolic patterns by the triple-shelled hollow ZMO/CMO particle-assisted LDI-MS and used machine learning to distinguish hepatocellular carcinoma from healthy controls with an accuracy of 86.96%.This chapter realizes the synthesis of triple-layer hollow ZMO/CMO composite materials for the first time and explores the influence of hollow layers number in the material on LDI matrix performance,laying a foundation for designing inorganic nanomaterials for clinical metabolic analysis.In summary,Our work designed three novel MOF-based LDI matrices with multiple physical effects,explored the relationship between material physical effects and LDI MS performance,and applied them to the clinical metabolic diagnosis of gynecological cancers and liver cancer,respectively.Our work proposed a potential clinical metabolic analysis platform for cancer diagnosis and contributed to understanding LDI MS mechanisms and the rational design of matrix materials.